Slinging mud at El DiabloA couple of months ago, on this blog we discussed a study by Busby and Messiersentitled ‘‘Cancer near Trawsfynydd Nuclear Power Station in Wales,UK: A Cross-Sectional Cohort Study”, and which had a couple of shortcomings…or, in otherwords…it was pretty bad. If you want to know why and how, have a look at “TheStory that wasn’t one…” (link).The paper was published in ‘Jacobs Journal of Epidemiology and PreventiveMedicine’, and if you have never heard of that…you are still not the only one! It isone of those new open access journals that features prominently on Beall’s list ofpredatory, or in other words if-you-pay-we-will-publish-anything, journals (link).What was also slightly dodgy about that whole study was that one of the authors(Busby, for the record) is on the journal’s Editorial Board, which may well havehelped getting a paper with such errors published.Anyway, that was a previous blog post and if you are interested in it you can find the link above (or elsewhere on The Fun Police site). Interestingly, the same author and Editorial Board member has recently published anew study in, of course, the same journal. It was published online on September 14,2016, and as such got published within two months of submission of the first draft.This implies it was an exceptionally well-written and scientifically accurate firstversion of the paper, the peer-review and journal are very fast, or the peer-reviewmay not have been up to scratch…or of course a combination of the three. It is openaccess, and you can find it by clicking here (link).Enfin…The paper is entitled “Is there Evidence of Adverse Health Effects Near US NuclearInstallations? Infant Mortality in Coastal Communities near The Diablo CanyonNuclear Power Station in California, 1989-2012”. That is quite a mouth full, but insummary the study looks at whether there is a correlation between living on acoastal side close to the Diablo Canyon Nuclear Power Station and the number ofchildren dying in that area. The idea behind the study is not that bad. Some previous studies have shown thatliving close to a nuclear power station may be correlated with a number ofincreased disease risks, and some have proposed that releases from the nuclearpower station may have something to do with that. Busby in this study argues thatthis risk is an underestimate of the true risk because whereas previous studieslooked at population generally living within the vicinity of power stations, the realrisk comes from contamination down-wind and down-stream of those stations.Specifically for power stations built near the sea, which is quite a lot of them, thiswould result in contamination of coastal areas with radioactive materials. Indeed, as you may have guessed, Diablo Canyon Nuclear Plant in California is such aplace.So, Busby got the 1989-2012 births and infant deaths by year and by Zip code,summed them per area (zip code group) and divided the deaths by the births toobtain crude mortality rates per 1,000 births. For unknown reason to me, since hehad the annual data, he then decided to group this again in four 6-year periods.And then, finally, he divided these in a Coastal Group, which supposedly has thehighest exposure, an Inland Group, which has lower exposure but is still relativelyclose to the power station, and as a third group he used the average numbers acrossall of California. The paper comes with a nice figure which can be used to draw thecoastal and inland groups of zip codes, so well at least you can see what was done.Interestingly, there seem to be two sets of Inland “control areas”, which is a bitunclear, while also not all coastal areas have been included. This is all a bitambiguous, but let’s give him the benefit of the doubt.What first springs to mind when looking at the infant mortality rates is, quiteworryingly, how high they are compared to European countries (i.e. 4-7 per 1,000compared to 2-4ish in Europe as you can see here). That however, is a completelydifferent story…The general argument in the paper is that despite the fact these rates have gonedown in the coastal areas, the inland areas, and in California as a whole up to theyear 2000, they kept going down until 2007-2012 in California while in the Coastalareas they started to increase again post-2000. In the “intermediate” inland areasthe rate staid fairly stable at about 4 per 1,000 from 2,000 onwards. Busby’sargument then is that this had to have been the result of the releases from thenuclear power station, and he shows a nice and clear linear correlation between theinfant mortality rate in the coastal areas in the four periods with the cumulativeamount of tritium (as a marker of all releases from the plant) from the start of theoperations in 1986..This, in a nutshell, is the story. Could it be true? Yes it could. Is it a bit flimsy? It most definitely is.Despite the fact Busby had the data to look at this correlation on an annual level,thereby being able to look at temporal patterns in a much better way, he choose notto, and instead used four periods only. I personally find that odd. Moreover, there is a table in the paper which shows the number of births in each ofthe included areas (stratified by coastal, inland or California as a whole) for the fourperiods. After just one glance you should notice that whereas in all but one of thecoastal areas the number of births has steadily been decreasing over the timeperiod, in most of the inland areas this has been increasing; in fact, the overalldecrease is almost entirely the result of a mass exodus in the area of San LuisObispo. Let’s think about that a bit….….so in the coastal areas the population has decreasing by about 20% over that timeperiod, while in the inland areas it has decreased by less than 5%. If the number ofinfants dying remained more or less stable (for sake of argument), the observedchanges in rates would be exactly as shown in the figure – as a result of demographicchanges only! Is that likely? Well that depends who is actually leaving (or entering) the areas.Indeed, although I don’t know the areas it seems very plausible that in the 24 yearperiod much will have changed. Someone else pointed that out and said it was theresult of Busby not taking the changes in Hispanic/White birth rates in the studyarea into account because, apparently, the infant mortality rate in Hispanics ishigher. Busby shows the percentages of Hispanic births over time in the three areasand shows that this cannot be the explanation (conveniently, this is not aggregatedby the same time periods, so cannot be directly linked). This could have easily beenmodelled statistically, such that the effect of the percentage of Hispanic/Whitebirths was taken into account (and what about a, presumably, increased percentageof mixed-race births? How would that change the estimates?). This is calledmultivariate regression methods, and if you don’t know what this is; for the purposeof this article it is easiest to remember that it is easy to do and Busby has access tothe data to do this. There are only very few reasons why someone would not do multivariate regressionmodels to adjust for important confounding factors (for confounding click here):1)It is the start of the data analyses, and a researcher just wants to see what is going on at face value. Multivariate analyses will follow after.2)Multivariate analyses are not needed because it is a randomizedcontrolled trial and all confounders are randomly distributed in the groups(note: this is NOT the case here). And even then it is often done anyway, just in case.3)You want to make a point, and it shows very clearly in straightforwardcomparison, but not when you to better, multivariate analyses.Indeed, point three is dodgy science, and problematic! I talked about this in my lastblog post as well (reference to “Alcohol and ‘fact’ checking in Ireland” here). Itunfortunately happens a lot; most notably because it often fits so nicely with thepoint we are trying to make, so why look further…this is called confirmation bias. And I strongly suspect this is what has been done in this paper too. A point wellmade, so why spoil it by making it more complicated?!?!Busby has the annual data and he has the racial demographics of the births (at leastat area level), so why not look at it? But lets go back to those differences in migration rates (remember, the 20% vs 5%)and think about multivariate analyses a bit further. Say for example that it is mostlyyoung people moving out of the coastal areas and into the inland areas, what wouldhappen then? The result would be that with less births (we know that from thepaper), and with infant mortality rates going up (we know that from the paper too),that the percentage of birth to older parents probably has gone up too. Maternalage is directly related to infant mortality rate in the US (here is a link to a paper forthe US (link1 and link2), so that could explain what we are seeing in the paper aswell, and therefore we may not need the contamination from the nuclear plant as afactor to explain the observed patterns.Can we find out?I did a quick online search, and was not able to quickly find longitudinal data(please let me know if you know how to get this), but the 2010(ish) median age foreach of the areas in the study is easy to find. That will work just as an indication…So the range for the coastal areas is about 28 to 57 years of age (median ~44 years),and for the inland areas the range is about 27 to 45 years (median ~37 years). Inother words….yes indeed, the population in the coastal areas is older on averagethan the inland area (at least in the 2007-2012 time stratum), and this could explainthe observed differences.My point here is not that the cause for these differences in infant mortality rates isdefinitely not radiation exposure, I don’t know that (although in my opinion this ishighly unlikely), but that with relatively little effort I found a possible otherexplanation. And it is fairly straightforward to come up with possible otherdemographic factors as well; for example, maybe socio-economic status differsbetween the areas, or income (in fact, the same sources showed that in 2010ishmedian household income was comparable in both areas, and was about 45k, butwas much higher in California as a whole (58k) which may explain that difference).And maybe, if these are poorer areas, there is less investment in healthcare or morepeople will have less access to healthcare, just to name some other things. My main point is that all these other factors could easily have been included in theanalyses, and these data are all available (probably for free). Just to recap; thereare three reasons why you would not do multivariate analyses when the data areavailable……..and only one seems relevant to this particular study. As a side note, since I came across this when doing a bit of googling about the area,and thought it would be nice to illustrate the correlation with contamination issueas well. How about the following alternative hypothesis?I came across the following report (full report link) entitled “Pismo Beach Fecalcontamination source identification study”. Pismo beach is one of the coastal areasin the study, and the report describes a study to identity the biological sources offecal contamination as well as the physical and environmental factors that influencethe levels of bacteria in the ocean waters at Pismo Beach, and was conducted in2008 (you know, in the final 2007-2012 time block). So contamination must havebeen ongoing in the preceding years….I mean, you know, …..swimming in waterpolluted with fecal residue, let alone swallowing it… just sayin’…*Anyway, personally, I don’t think studies like this should be published (or at least not without properly addressing their shortcomings); especially not in a highly emotivearea of environmental health such as this one. There are enough worries related toradiation and nuclear power as it is, and these things don’t help. A comparable worry has been ongoing in the UK in relation to childhood leukaemiaclusters in areas surrounding nuclear power stations, and whether this could be theresult of the release of radioactive contamination from the sites. The UKIndependent Advisory Committee on Medical Aspects of Radiation in the Environment (COMARE) has conducted a thorough and exhaustive review of all available evidenceand concluded that a more likely explanation, at least in part, is the result ofurban/rural population-mixing and associated changes in viral loads (disclaimer: Iam a member of that committee). If you are interested, a link to the full reportwhich can be downloaded for free, can be found here (link).